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Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
Author(s)
Leite Coelho da Silva, Felipe
Universidade Federal Rural do Rio de Janeiro
da Costa, Kleyton
Universidade Federal Rural do Rio de Janeiro
Canas Rodrigues, Paulo
Universidade Federal da Bahia
Salas, Rodrigo
University of Valparaíso
Date Issued
14 de enero de 2022
Type
Article
Volume
15
Issue
2
Series/Report No.
Energies
Start Page
588
End Page
588
ISSN
1996-1073
Abstract
Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.